Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimising lead qualification through machine learning: A customer data-driven approach Subject The topic of the resource cold leads; decision tree; hot leads; logistic regression; machine learning; supervised ML algorithms Description An account of the resource Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low codeno code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms. Henry Stewart Publications. Creator An entity primarily responsible for making the resource Helen J.V.L.; Moorthy V.; Sai C.; Joji B. Source A related resource from which the described resource is derived Applied Marketing Analytics, Vol-10, No. 3, pp. 255-270. Publisher An entity responsible for making the resource available Henry Stewart Publications Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.69554/ETXF7251" target="_blank" rel="noreferrer noopener">https://doi.org/10.69554/ETXF7251</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213383861&amp;doi=10.69554%2FETXF7251&amp;partnerID=40&amp;md5=049aa95eff0da5dc89531c56444bf6cb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213383861&amp;doi=10.69554%2fETXF7251&amp;partnerID=40&amp;md5=049aa95eff0da5dc89531c56444bf6cb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 20547544 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Helen J.V.L., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Moorthy V., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Sai C., Enterprise Solution Architect, Airtel Business, India; Joji B., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India